Evidence map›Paper›PMID 41555583›Full record

ArticleAngewandte Chemie (International ed. in English)2026

Predictive, Data-Driven Design of Red-Light Photoredox Catalysts for C─Heteroatom Bond Formation.

Amir Gizatullin, Tingting Yuan, Sascha Grotjahn, Luigi Cavallo, Burkhard König, Chen Zhu, Magnus Rueping

Abstract read
In one paragraph

Article in Angewandte Chemie (International ed. in English), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Amir Gizatullin *KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Tingting Yuan *KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Sascha GrotjahnFakultät für Chemie und Pharmazie, Universität Regensburg, Regensburg, 93053, Germany.
Luigi CavalloKAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Burkhard KönigFakultät für Chemie und Pharmazie, Universität Regensburg, Regensburg, 93053, Germany.
Chen ZhuNingbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Ningbo, 315200, China.
Magnus RuepingKAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.ORCID https://orcid.org/0000-0003-4580-5227

Funding

Deutsche Forschungsgemeinschaft 2620 - 426795949King Abdullah University of Science and TechnologyNational Natural Science Foundation of China 2240011820National Natural Science Foundation of China 22401164National Science and Technology Major Project 2025ZD0121904
6 · The paper itself

Abstract

Photocatalysis is a powerful tool for the synthesis of organic molecules, yet its widespread application is hindered by the dependence on high-energy light sources and expensive metal-based catalysts, which can limit scalability and environmental sustainability. In this study, we present a modular design strategy for organic dyes engineered for efficient red-light absorption, enabling photocatalytic reactions under low-energy irradiation. Our findings establish a clear relationship between the oxidation potential of the photocatalyst and the nature of its donor moiety, as well as between the reduction potential and the electronic characteristics of its core structure. Moreover, we demonstrate that the E

Indexed as

C─heteroatom bondsCyanoarenesNickelPhotocatalystsRed light

Identifiers

PMID41555583
PMCPMC12929924

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.